Determining semantic similarity of texts based on sub-sections thereof

ABSTRACT

Systems and methods are provided to compare a target sample of text to a set of textual records, each textual record including a sample of text and an indication of one or more segments of text within the sample of text. Semantic similarity values between the target sample of text and each of the textual records are determined. Determining a particular semantic similarity value between the target sample of text and a particular textual record of the corpus includes: (i) determining individual semantic similarity values between the target sample of text and each of the segments of text indicated by the particular textual record, and (ii) generating the particular semantic similarity value between the target sample of text and the particular textual record based on the individual semantic similarity values. A textual record is then selected based on the semantic similarities.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of and claims priority to U.S. patentapplication Ser. No. 16/362,187, filed Mar. 22, 2019, which is herebyincorporated by reference in its entirety

BACKGROUND

It is beneficial in a variety of applications to determine a similaritybetween samples of text or to otherwise compare samples of text. Thiscan be done in order to identify, from a corpus of text samples, one ormore samples that are similar in some respect to a target sample oftext. For example, a target sample of text could contain a user'sdescription of an information technology problem, and the target samplecould be compared to articles in a database that describe a variety ofinformation technology topics and/or solutions to information technologyproblems. By comparing the user's description of the problem to thearticles in the database, one or more “most relevant” or otherwisesimilar articles can be provided to the user and/or to a technician inorder to efficiently guide them to a solution.

SUMMARY

Natural language processing or other methods can be used to comparesamples of text. This can be done in order to find patterns within thesamples of text, to identify samples that are relevant to a query orotherwise similar to a target sample of text, or to provide some otherbenefit. For example, a query can include a sample of text describing aproblem that a user is experiencing. Similarity values can then bedetermined between the query text and the text of articles within adatabase, so that one or more articles that are most similar to thequery may be provided to the user. This comparison may improve thequality of results provided to the user and/or reduce the amount of timespent by the user before determining and implementing a solution to theproblem.

However, when articles or other samples of text are large (e.g.,relative to query texts), it can become difficult to compare thearticles with query text in a way that generates useful results. Forexample, longer articles may contain sub-sections that are relevant to aparticular query text, but also contain other portions that are notrelevant. As a result, such relevant articles might not be detected, dueto the effects of the non-relevant portions of the articles.

To address this issue, articles or other large samples of text may bepartitioned into segments. The segments may be determined according tosentences, paragraphs, or other punctuation within the large sample oftext, using a segmenting algorithm that has been trained based on asample of query texts, or according to some other method. This could bedone so that the segments of text are of similar size to the queries towhich they are likely to be compared. Similarity values can then be madebetween a query text and each segment of each of the large samples oftext. An overall similarity value for a particular large sample of textcan then be determined based on the similarity values for each of thetext segments within the large sample of text. This could include addingtogether the segment similarity values, determining a maximum segmentsimilarity value, determining how many of the segment similarity valuesexceed a threshold, or some other method. The overall similarity valuesfor the large samples of text can then be used to select large samplesof text that are relevant to a query text or to facilitate some otherapplication.

Accordingly, a first example embodiment may involve acomputer-implemented method that includes: (i) obtaining, by a serverdevice, a corpus of textual records, where each of the textual recordsincludes a sample of text and an indication of one or more segments oftext within the sample of text, wherein at least one of the textualrecords includes an indication of at least two segments of text withinthe sample of text; (ii) obtaining, by the server device and from aclient device, a target sample of text; (iii) determining, by the serverdevice, semantic similarity values between the target sample of text andeach of the textual records; (iv) based on the semantic similarityvalues, selecting, by the server device and from the corpus, a textualrecord with a semantic similarity indicating that the textual record ismore similar to the target sample of text than any other of the textualrecords; and (v) providing, by the server device and to the clientdevice, a representation of the textual record. Determining a particularsemantic similarity value between the target sample of text and aparticular textual record of the corpus includes: (i) determiningindividual semantic similarity values between the target sample of textand each of the segments of text indicated by the particular textualrecord, and (ii) generating the particular semantic similarity valuebetween the target sample of text and the particular textual recordbased on the individual semantic similarity values.

In a second example embodiment, an article of manufacture may include anon-transitory computer-readable medium, having stored thereon programinstructions that, upon execution by a computing system, cause thecomputing system to perform operations in accordance with the firstexample embodiment.

In a third example embodiment, a computing system may include at leastone processor, as well as memory and program instructions. The programinstructions may be stored in the memory, and upon execution by the atleast one processor, cause the computing system to perform operations inaccordance with the first example embodiment.

In a fourth example embodiment, a system may include various means forcarrying out each of the operations of the first example embodiment.

In some embodiments, each of the textual records includes a respectiveindication of at least two segments of text within the respective sampleof text. That is, all of the textual records can include multiplesegments of text. Alternatively, one or more of the textual records mayinclude only a single segment of text. For example, shorter samples oftext may not be broken up into multiple segments.

In some embodiments, determining an individual semantic similarity valuebetween the target sample of text and a particular segment of textwithin the particular textual record includes: (i) obtaining a vectorrepresentation of the target sample of text, wherein the vectorrepresentation of the target sample of text includes at least one of (a)word vectors that describe, in a first semantically-encoded vectorspace, a meaning of respective words of the target sample of text, or(b) a paragraph vector that describes, in a second semantically-encodedvector space, a meaning of multiple words of the target sample of text;(ii) obtaining a vector representation of the particular segment oftext, wherein the vector representation of the particular segment oftext includes at least one of (a) word vectors that describe, in thefirst semantically-encoded vector space, a meaning of respective wordsof the particular segment of text, or (b) a paragraph vector thatdescribes, in the second semantically-encoded vector space, a meaning ofmultiple words of the particular segment of text; and (iii) determininga semantic similarity value between the vector representation of thetarget sample of text and the vector representation of the particularsegment of text.

In some embodiments, generating the particular semantic similarity valuebetween the target sample of text and the particular textual recordbased on the individual semantic similarity values includes: (i)comparing, to a threshold similarity level, each of the individualsemantic similarity values between the target sample of text and each ofthe segments of text indicated by the particular textual record; and(ii) determining a number of the individual semantic similarity valuesthat exceeded the threshold similarity level as the particular semanticsimilarity value.

In some embodiments, generating the particular semantic similarity valuebetween the target sample of text and the particular textual recordbased on the individual semantic similarity values includes: (i)generating a sum of the individual semantic similarity values betweenthe target sample of text and each of the segments of text indicated bythe particular textual record; and (ii) normalizing the sum to a numberof segments of text indicated by the particular textual record.

In some embodiments, the indications of one or more segments of textwithin each sample of text indicate non-overlapping segments of text.

In some embodiments, the indications of one or more segments of textwithin each sample of text indicate segments of text that each representone or more discrete sentences.

In some embodiments, the method performed additionally includes: (i)obtaining, from one or more client devices, a plurality of queries,wherein each query includes a sample of text; (ii) training a machinelearning model, based on the plurality of queries, to predict relatedsegments of text within samples of text; and (iii) applying the machinelearning model as trained to the corpus of textual records to generatethe indications of one or more segments of text within the samples oftext of the corpus of textual records.

These, as well as other embodiments, aspects, advantages, andalternatives, will become apparent to those of ordinary skill in the artby reading the following detailed description, with reference whereappropriate to the accompanying drawings. Further, this summary andother descriptions and figures provided herein are intended toillustrate embodiments by way of example only and, as such, thatnumerous variations are possible. For instance, structural elements andprocess steps can be rearranged, combined, distributed, eliminated, orotherwise changed, while remaining within the scope of the embodimentsas claimed.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a schematic drawing of a computing device, inaccordance with example embodiments.

FIG. 2 illustrates a schematic drawing of a server device cluster, inaccordance with example embodiments.

FIG. 3 depicts a remote network management architecture, in accordancewith example embodiments.

FIG. 4 depicts a communication environment involving a remote networkmanagement architecture, in accordance with example embodiments.

FIG. 5A depicts another communication environment involving a remotenetwork management architecture, in accordance with example embodiments.

FIG. 5B is a flow chart, in accordance with example embodiments.

FIG. 6 depicts an incident report, in accordance with exampleembodiments.

FIG. 7 depicts a database query architecture, in accordance with exampleembodiments.

FIG. 8 depicts samples of text, in accordance with example embodiments.

FIG. 9A depicts an artificial neural network (ANN) configured forlearning the contextual meanings of words, in accordance with exampleembodiments.

FIG. 9B depicts a set of training data for the ANN of FIG. 9A, inaccordance with example embodiments.

FIG. 9C depicts a set of training data for the ANN of FIG. 9A, inaccordance with example embodiments.

FIG. 9D depicts a set of training data for the ANN of FIG. 9A, inaccordance with example embodiments.

FIG. 10A depicts training an ANN for paragraph vectors, in accordancewith example embodiments.

FIG. 10B depicts training an ANN for paragraph vectors, in accordancewith example embodiments.

FIG. 10C depicts training an ANN for paragraph vectors, in accordancewith example embodiments.

FIG. 10D depicts using a trained ANN to determine the paragraph vectorof a previously unseen paragraph, in accordance with exampleembodiments.

FIG. 11 is a flow chart, in accordance with example embodiments.

DETAILED DESCRIPTION

Example methods, devices, and systems are described herein. It should beunderstood that the words “example” and “exemplary” are used herein tomean “serving as an example, instance, or illustration.” Any embodimentor feature described herein as being an “example” or “exemplary” is notnecessarily to be construed as preferred or advantageous over otherembodiments or features unless stated as such. Thus, other embodimentscan be utilized and other changes can be made without departing from thescope of the subject matter presented herein.

Accordingly, the example embodiments described herein are not meant tobe limiting. It will be readily understood that the aspects of thepresent disclosure, as generally described herein, and illustrated inthe figures, can be arranged, substituted, combined, separated, anddesigned in a wide variety of different configurations. For example, theseparation of features into “client” and “server” components may occurin a number of ways.

Further, unless context suggests otherwise, the features illustrated ineach of the figures may be used in combination with one another. Thus,the figures should be generally viewed as component aspects of one ormore overall embodiments, with the understanding that not allillustrated features are necessary for each embodiment.

Additionally, any enumeration of elements, blocks, or steps in thisspecification or the claims is for purposes of clarity. Thus, suchenumeration should not be interpreted to require or imply that theseelements, blocks, or steps adhere to a particular arrangement or arecarried out in a particular order.

I. INTRODUCTION

A large enterprise is a complex entity with many interrelatedoperations. Some of these are found across the enterprise, such as humanresources (HR), supply chain, information technology (IT), and finance.However, each enterprise also has its own unique operations that provideessential capabilities and/or create competitive advantages.

To support widely-implemented operations, enterprises typically useoff-the-shelf software applications, such as customer relationshipmanagement (CRM) and human capital management (HCM) packages. However,they may also need custom software applications to meet their own uniquerequirements. A large enterprise often has dozens or hundreds of thesecustom software applications. Nonetheless, the advantages provided bythe embodiments herein are not limited to large enterprises and may beapplicable to an enterprise, or any other type of organization, of anysize.

Many such software applications are developed by individual departmentswithin the enterprise. These range from simple spreadsheets tocustom-built software tools and databases. But the proliferation ofsiloed custom software applications has numerous disadvantages. Itnegatively impacts an enterprise's ability to run and grow itsoperations, innovate, and meet regulatory requirements. The enterprisemay find it difficult to integrate, streamline and enhance itsoperations due to lack of a single system that unifies its subsystemsand data.

To efficiently create custom applications, enterprises would benefitfrom a remotely-hosted application platform that eliminates unnecessarydevelopment complexity. The goal of such a platform would be to reducetime-consuming, repetitive application development tasks so thatsoftware engineers and individuals in other roles can focus ondeveloping unique, high-value features.

In order to achieve this goal, the concept of Application Platform as aService (aPaaS) is introduced, to intelligently automate workflowsthroughout the enterprise. An aPaaS system is hosted remotely from theenterprise, but may access data, applications, and services within theenterprise by way of secure connections. Such an aPaaS system may have anumber of advantageous capabilities and characteristics. Theseadvantages and characteristics may be able to improve the enterprise'soperations and workflow for IT, HR, CRM, customer service, applicationdevelopment, and security.

The aPaaS system may support development and execution ofmodel-view-controller (MVC) applications. MVC applications divide theirfunctionality into three interconnected parts (model, view, andcontroller) in order to isolate representations of information from themanner in which the information is presented to the user, therebyallowing for efficient code reuse and parallel development. Theseapplications may be web-based, and offer create, read, update, delete(CRUD) capabilities. This allows new applications to be built on acommon application infrastructure.

The aPaaS system may support standardized application components, suchas a standardized set of widgets for graphical user interface (GUI)development. In this way, applications built using the aPaaS system havea common look and feel. Other software components and modules may bestandardized as well. In some cases, this look and feel can be brandedor skinned with an enterprise's custom logos and/or color schemes.

The aPaaS system may support the ability to configure the behavior ofapplications using metadata. This allows application behaviors to berapidly adapted to meet specific needs. Such an approach reducesdevelopment time and increases flexibility. Further, the aPaaS systemmay support GUI tools that facilitate metadata creation and management,thus reducing errors in the metadata.

The aPaaS system may support clearly-defined interfaces betweenapplications, so that software developers can avoid unwantedinter-application dependencies. Thus, the aPaaS system may implement aservice layer in which persistent state information and other data arestored.

The aPaaS system may support a rich set of integration features so thatthe applications thereon can interact with legacy applications andthird-party applications. For instance, the aPaaS system may support acustom employee-onboarding system that integrates with legacy HR, IT,and accounting systems.

The aPaaS system may support enterprise-grade security. Furthermore,since the aPaaS system may be remotely hosted, it should also utilizesecurity procedures when it interacts with systems in the enterprise orthird-party networks and services hosted outside of the enterprise. Forexample, the aPaaS system may be configured to share data amongst theenterprise and other parties to detect and identify common securitythreats.

Other features, functionality, and advantages of an aPaaS system mayexist. This description is for purpose of example and is not intended tobe limiting.

As an example of the aPaaS development process, a software developer maybe tasked to create a new application using the aPaaS system. First, thedeveloper may define the data model, which specifies the types of datathat the application uses and the relationships therebetween. Then, viaa GUI of the aPaaS system, the developer enters (e.g., uploads) the datamodel. The aPaaS system automatically creates all of the correspondingdatabase tables, fields, and relationships, which can then be accessedvia an object-oriented services layer.

In addition, the aPaaS system can also build a fully-functional MVCapplication with client-side interfaces and server-side CRUD logic. Thisgenerated application may serve as the basis of further development forthe user. Advantageously, the developer does not have to spend a largeamount of time on basic application functionality. Further, since theapplication may be web-based, it can be accessed from anyInternet-enabled client device. Alternatively or additionally, a localcopy of the application may be able to be accessed, for instance, whenInternet service is not available.

The aPaaS system may also support a rich set of pre-definedfunctionality that can be added to applications. These features includesupport for searching, email, templating, workflow design, reporting,analytics, social media, scripting, mobile-friendly output, andcustomized GUIs.

The following embodiments describe architectural and functional aspectsof example aPaaS systems, as well as the features and advantagesthereof.

II. EXAMPLE COMPUTING DEVICES AND CLOUD-BASED COMPUTING ENVIRONMENTS

FIG. 1 is a simplified block diagram exemplifying a computing device100, illustrating some of the components that could be included in acomputing device arranged to operate in accordance with the embodimentsherein. Computing device 100 could be a client device (e.g., a deviceactively operated by a user), a server device (e.g., a device thatprovides computational services to client devices), or some other typeof computational platform. Some server devices may operate as clientdevices from time to time in order to perform particular operations, andsome client devices may incorporate server features.

In this example, computing device 100 includes processor 102, memory104, network interface 106, and an input/output unit 108, all of whichmay be coupled by a system bus 110 or a similar mechanism. In someembodiments, computing device 100 may include other components and/orperipheral devices (e.g., detachable storage, printers, and so on).

Processor 102 may be one or more of any type of computer processingelement, such as a central processing unit (CPU), a co-processor (e.g.,a mathematics, graphics, or encryption co-processor), a digital signalprocessor (DSP), a network processor, and/or a form of integratedcircuit or controller that performs processor operations. In some cases,processor 102 may be one or more single-core processors. In other cases,processor 102 may be one or more multi-core processors with multipleindependent processing units. Processor 102 may also include registermemory for temporarily storing instructions being executed and relateddata, as well as cache memory for temporarily storing recently-usedinstructions and data.

Memory 104 may be any form of computer-usable memory, including but notlimited to random access memory (RAM), read-only memory (ROM), andnon-volatile memory (e.g., flash memory, hard disk drives, solid statedrives, compact discs (CDs), digital video discs (DVDs), and/or tapestorage). Thus, memory 104 represents both main memory units, as well aslong-term storage. Other types of memory may include biological memory.

Memory 104 may store program instructions and/or data on which programinstructions may operate. By way of example, memory 104 may store theseprogram instructions on a non-transitory, computer-readable medium, suchthat the instructions are executable by processor 102 to carry out anyof the methods, processes, or operations disclosed in this specificationor the accompanying drawings.

As shown in FIG. 1, memory 104 may include firmware 104A, kernel 104B,and/or applications 104C. Firmware 104A may be program code used to bootor otherwise initiate some or all of computing device 100. Kernel 104Bmay be an operating system, including modules for memory management,scheduling and management of processes, input/output, and communication.Kernel 104B may also include device drivers that allow the operatingsystem to communicate with the hardware modules (e.g., memory units,networking interfaces, ports, and busses), of computing device 100.Applications 104C may be one or more user-space software programs, suchas web browsers or email clients, as well as any software libraries usedby these programs. Memory 104 may also store data used by these andother programs and applications.

Network interface 106 may take the form of one or more wirelineinterfaces, such as Ethernet (e.g., Fast Ethernet, Gigabit Ethernet, andso on). Network interface 106 may also support communication over one ormore non-Ethernet media, such as coaxial cables or power lines, or overwide-area media, such as Synchronous Optical Networking (SONET) ordigital subscriber line (DSL) technologies. Network interface 106 mayadditionally take the form of one or more wireless interfaces, such asIEEE 802.11 (Wifi), BLUETOOTH®, global positioning system (GPS), or awide-area wireless interface. However, other forms of physical layerinterfaces and other types of standard or proprietary communicationprotocols may be used over network interface 106. Furthermore, networkinterface 106 may comprise multiple physical interfaces. For instance,some embodiments of computing device 100 may include Ethernet,BLUETOOTH®, and Wifi interfaces.

Input/output unit 108 may facilitate user and peripheral deviceinteraction with computing device 100. Input/output unit 108 may includeone or more types of input devices, such as a keyboard, a mouse, a touchscreen, and so on. Similarly, input/output unit 108 may include one ormore types of output devices, such as a screen, monitor, printer, and/orone or more light emitting diodes (LEDs). Additionally or alternatively,computing device 100 may communicate with other devices using auniversal serial bus (USB) or high-definition multimedia interface(HDMI) port interface, for example.

In some embodiments, one or more computing devices like computing device100 may be deployed to support an aPaaS architecture. The exact physicallocation, connectivity, and configuration of these computing devices maybe unknown and/or unimportant to client devices. Accordingly, thecomputing devices may be referred to as “cloud-based” devices that maybe housed at various remote data center locations.

FIG. 2 depicts a cloud-based server cluster 200 in accordance withexample embodiments. In FIG. 2, operations of a computing device (e.g.,computing device 100) may be distributed between server devices 202,data storage 204, and routers 206, all of which may be connected bylocal cluster network 208. The number of server devices 202, datastorages 204, and routers 206 in server cluster 200 may depend on thecomputing task(s) and/or applications assigned to server cluster 200.

For example, server devices 202 can be configured to perform variouscomputing tasks of computing device 100. Thus, computing tasks can bedistributed among one or more of server devices 202. To the extent thatthese computing tasks can be performed in parallel, such a distributionof tasks may reduce the total time to complete these tasks and return aresult. For purpose of simplicity, both server cluster 200 andindividual server devices 202 may be referred to as a “server device.”This nomenclature should be understood to imply that one or moredistinct server devices, data storage devices, and cluster routers maybe involved in server device operations.

Data storage 204 may be data storage arrays that include drive arraycontrollers configured to manage read and write access to groups of harddisk drives and/or solid state drives. The drive array controllers,alone or in conjunction with server devices 202, may also be configuredto manage backup or redundant copies of the data stored in data storage204 to protect against drive failures or other types of failures thatprevent one or more of server devices 202 from accessing units of datastorage 204. Other types of memory aside from drives may be used.

Routers 206 may include networking equipment configured to provideinternal and external communications for server cluster 200. Forexample, routers 206 may include one or more packet-switching and/orrouting devices (including switches and/or gateways) configured toprovide (i) network communications between server devices 202 and datastorage 204 via local cluster network 208, and/or (ii) networkcommunications between the server cluster 200 and other devices viacommunication link 210 to network 212.

Additionally, the configuration of routers 206 can be based at least inpart on the data communication requirements of server devices 202 anddata storage 204, the latency and throughput of the local clusternetwork 208, the latency, throughput, and cost of communication link210, and/or other factors that may contribute to the cost, speed,fault-tolerance, resiliency, efficiency and/or other design goals of thesystem architecture.

As a possible example, data storage 204 may include any form ofdatabase, such as a structured query language (SQL) database. Varioustypes of data structures may store the information in such a database,including but not limited to tables, arrays, lists, trees, and tuples.Furthermore, any databases in data storage 204 may be monolithic ordistributed across multiple physical devices.

Server devices 202 may be configured to transmit data to and receivedata from data storage 204. This transmission and retrieval may take theform of SQL queries or other types of database queries, and the outputof such queries, respectively. Additional text, images, video, and/oraudio may be included as well. Furthermore, server devices 202 mayorganize the received data into web page representations. Such arepresentation may take the form of a markup language, such as thehypertext markup language (HTML), the extensible markup language (XML),or some other standardized or proprietary format. Moreover, serverdevices 202 may have the capability of executing various types ofcomputerized scripting languages, such as but not limited to Perl,Python, PHP Hypertext Preprocessor (PHP), Active Server Pages (ASP),JAVASCRIPT®, and so on. Computer program code written in these languagesmay facilitate the providing of web pages to client devices, as well asclient device interaction with the web pages.

III. EXAMPLE REMOTE NETWORK MANAGEMENT ARCHITECTURE

FIG. 3 depicts a remote network management architecture, in accordancewith example embodiments. This architecture includes three maincomponents, managed network 300, remote network management platform 320,and third-party networks 340, all connected by way of Internet 350.

Managed network 300 may be, for example, an enterprise network used byan entity for computing and communications tasks, as well as storage ofdata. Thus, managed network 300 may include client devices 302, serverdevices 304, routers 306, virtual machines 308, firewall 310, and/orproxy servers 312. Client devices 302 may be embodied by computingdevice 100, server devices 304 may be embodied by computing device 100or server cluster 200, and routers 306 may be any type of router,switch, or gateway.

Virtual machines 308 may be embodied by one or more of computing device100 or server cluster 200. In general, a virtual machine is an emulationof a computing system, and mimics the functionality (e.g., processor,memory, and communication resources) of a physical computer. Onephysical computing system, such as server cluster 200, may support up tothousands of individual virtual machines. In some embodiments, virtualmachines 308 may be managed by a centralized server device orapplication that facilitates allocation of physical computing resourcesto individual virtual machines, as well as performance and errorreporting. Enterprises often employ virtual machines in order toallocate computing resources in an efficient, as needed fashion.Providers of virtualized computing systems include VMWARE® andMICROSOFT®.

Firewall 310 may be one or more specialized routers or server devicesthat protect managed network 300 from unauthorized attempts to accessthe devices, applications, and services therein, while allowingauthorized communication that is initiated from managed network 300.Firewall 310 may also provide intrusion detection, web filtering, virusscanning, application-layer gateways, and other applications orservices. In some embodiments not shown in FIG. 3, managed network 300may include one or more virtual private network (VPN) gateways withwhich it communicates with remote network management platform 320 (seebelow).

Managed network 300 may also include one or more proxy servers 312. Anembodiment of proxy servers 312 may be a server device that facilitatescommunication and movement of data between managed network 300, remotenetwork management platform 320, and third-party networks 340. Inparticular, proxy servers 312 may be able to establish and maintainsecure communication sessions with one or more computational instancesof remote network management platform 320. By way of such a session,remote network management platform 320 may be able to discover andmanage aspects of the architecture and configuration of managed network300 and its components. Possibly with the assistance of proxy servers312, remote network management platform 320 may also be able to discoverand manage aspects of third-party networks 340 that are used by managednetwork 300.

Firewalls, such as firewall 310, typically deny all communicationsessions that are incoming by way of Internet 350, unless such a sessionwas ultimately initiated from behind the firewall (i.e., from a deviceon managed network 300) or the firewall has been explicitly configuredto support the session. By placing proxy servers 312 behind firewall 310(e.g., within managed network 300 and protected by firewall 310), proxyservers 312 may be able to initiate these communication sessions throughfirewall 310. Thus, firewall 310 might not have to be specificallyconfigured to support incoming sessions from remote network managementplatform 320, thereby avoiding potential security risks to managednetwork 300.

In some cases, managed network 300 may consist of a few devices and asmall number of networks. In other deployments, managed network 300 mayspan multiple physical locations and include hundreds of networks andhundreds of thousands of devices. Thus, the architecture depicted inFIG. 3 is capable of scaling up or down by orders of magnitude.

Furthermore, depending on the size, architecture, and connectivity ofmanaged network 300, a varying number of proxy servers 312 may bedeployed therein. For example, each one of proxy servers 312 may beresponsible for communicating with remote network management platform320 regarding a portion of managed network 300. Alternatively oradditionally, sets of two or more proxy servers may be assigned to sucha portion of managed network 300 for purposes of load balancing,redundancy, and/or high availability.

Remote network management platform 320 is a hosted environment thatprovides aPaaS services to users, particularly to the operators ofmanaged network 300. These services may take the form of web-basedportals, for instance. Thus, a user can securely access remote networkmanagement platform 320 from, for instance, client devices 302, orpotentially from a client device outside of managed network 300. By wayof the web-based portals, users may design, test, and deployapplications, generate reports, view analytics, and perform other tasks.

As shown in FIG. 3, remote network management platform 320 includes fourcomputational instances 322, 324, 326, and 328. Each of these instancesmay represent one or more server devices and/or one or more databasesthat provide a set of web portals, services, and applications (e.g., awholly-functioning aPaaS system) available to a particular customer. Insome cases, a single customer may use multiple computational instances.For example, managed network 300 may be an enterprise customer of remotenetwork management platform 320, and may use computational instances322, 324, and 326. The reason for providing multiple instances to onecustomer is that the customer may wish to independently develop, test,and deploy its applications and services. Thus, computational instance322 may be dedicated to application development related to managednetwork 300, computational instance 324 may be dedicated to testingthese applications, and computational instance 326 may be dedicated tothe live operation of tested applications and services. A computationalinstance may also be referred to as a hosted instance, a remoteinstance, a customer instance, or by some other designation. Anyapplication deployed onto a computational instance may be a scopedapplication, in that its access to databases within the computationalinstance can be restricted to certain elements therein (e.g., one ormore particular database tables or particular rows with one or moredatabase tables).

For purpose of clarity, the disclosure herein refers to the physicalhardware, software, and arrangement thereof as a “computationalinstance.” Note that users may colloquially refer to the graphical userinterfaces provided thereby as “instances.” But unless it is definedotherwise herein, a “computational instance” is a computing systemdisposed within remote network management platform 320.

The multi-instance architecture of remote network management platform320 is in contrast to conventional multi-tenant architectures, overwhich multi-instance architectures exhibit several advantages. Inmulti-tenant architectures, data from different customers (e.g.,enterprises) are comingled in a single database. While these customers'data are separate from one another, the separation is enforced by thesoftware that operates the single database. As a consequence, a securitybreach in this system may impact all customers' data, creatingadditional risk, especially for entities subject to governmental,healthcare, and/or financial regulation. Furthermore, any databaseoperations that impact one customer will likely impact all customerssharing that database. Thus, if there is an outage due to hardware orsoftware errors, this outage affects all such customers. Likewise, ifthe database is to be upgraded to meet the needs of one customer, itwill be unavailable to all customers during the upgrade process. Often,such maintenance windows will be long, due to the size of the shareddatabase.

In contrast, the multi-instance architecture provides each customer withits own database in a dedicated computing instance. This preventscomingling of customer data, and allows each instance to beindependently managed. For example, when one customer's instanceexperiences an outage due to errors or an upgrade, other computationalinstances are not impacted. Maintenance down time is limited because thedatabase only contains one customer's data. Further, the simpler designof the multi-instance architecture allows redundant copies of eachcustomer database and instance to be deployed in a geographicallydiverse fashion. This facilitates high availability, where the liveversion of the customer's instance can be moved when faults are detectedor maintenance is being performed.

In some embodiments, remote network management platform 320 may includeone or more central instances, controlled by the entity that operatesthis platform. Like a computational instance, a central instance mayinclude some number of physical or virtual servers and database devices.Such a central instance may serve as a repository for data that can beshared amongst at least some of the computational instances. Forinstance, definitions of common security threats that could occur on thecomputational instances, software packages that are commonly discoveredon the computational instances, and/or an application store forapplications that can be deployed to the computational instances mayreside in a central instance. Computational instances may communicatewith central instances by way of well-defined interfaces in order toobtain this data.

In order to support multiple computational instances in an efficientfashion, remote network management platform 320 may implement aplurality of these instances on a single hardware platform. For example,when the aPaaS system is implemented on a server cluster such as servercluster 200, it may operate a virtual machine that dedicates varyingamounts of computational, storage, and communication resources toinstances. But full virtualization of server cluster 200 might not benecessary, and other mechanisms may be used to separate instances. Insome examples, each instance may have a dedicated account and one ormore dedicated databases on server cluster 200. Alternatively,computational instance 322 may span multiple physical devices.

In some cases, a single server cluster of remote network managementplatform 320 may support multiple independent enterprises. Furthermore,as described below, remote network management platform 320 may includemultiple server clusters deployed in geographically diverse data centersin order to facilitate load balancing, redundancy, and/or highavailability.

Third-party networks 340 may be remote server devices (e.g., a pluralityof server clusters such as server cluster 200) that can be used foroutsourced computational, data storage, communication, and servicehosting operations. These servers may be virtualized (i.e., the serversmay be virtual machines). Examples of third-party networks 340 mayinclude AMAZON WEB SERVICES® and MICROSOFT® AZURE®. Like remote networkmanagement platform 320, multiple server clusters supporting third-partynetworks 340 may be deployed at geographically diverse locations forpurposes of load balancing, redundancy, and/or high availability.

Managed network 300 may use one or more of third-party networks 340 todeploy applications and services to its clients and customers. Forinstance, if managed network 300 provides online music streamingservices, third-party networks 340 may store the music files and provideweb interface and streaming capabilities. In this way, the enterprise ofmanaged network 300 does not have to build and maintain its own serversfor these operations.

Remote network management platform 320 may include modules thatintegrate with third-party networks 340 to expose virtual machines andmanaged services therein to managed network 300. The modules may allowusers to request virtual resources and provide flexible reporting forthird-party networks 340. In order to establish this functionality, auser from managed network 300 might first establish an account withthird-party networks 340, and request a set of associated resources.Then, the user may enter the account information into the appropriatemodules of remote network management platform 320. These modules maythen automatically discover the manageable resources in the account, andalso provide reports related to usage, performance, and billing.

Internet 350 may represent a portion of the global Internet. However,Internet 350 may alternatively represent a different type of network,such as a private wide-area or local-area packet-switched network.

FIG. 4 further illustrates the communication environment between managednetwork 300 and computational instance 322, and introduces additionalfeatures and alternative embodiments. In FIG. 4, computational instance322 is replicated across data centers 400A and 400B. These data centersmay be geographically distant from one another, perhaps in differentcities or different countries. Each data center includes supportequipment that facilitates communication with managed network 300, aswell as remote users.

In data center 400A, network traffic to and from external devices flowseither through VPN gateway 402A or firewall 404A. VPN gateway 402A maybe peered with VPN gateway 412 of managed network 300 by way of asecurity protocol such as Internet Protocol Security (IPSEC) orTransport Layer Security (TLS). Firewall 404A may be configured to allowaccess from authorized users, such as user 414 and remote user 416, andto deny access to unauthorized users. By way of firewall 404A, theseusers may access computational instance 322, and possibly othercomputational instances. Load balancer 406A may be used to distributetraffic amongst one or more physical or virtual server devices that hostcomputational instance 322. Load balancer 406A may simplify user accessby hiding the internal configuration of data center 400A, (e.g.,computational instance 322) from client devices. For instance, ifcomputational instance 322 includes multiple physical or virtualcomputing devices that share access to multiple databases, load balancer406A may distribute network traffic and processing tasks across thesecomputing devices and databases so that no one computing device ordatabase is significantly busier than the others. In some embodiments,computational instance 322 may include VPN gateway 402A, firewall 404A,and load balancer 406A.

Data center 400B may include its own versions of the components in datacenter 400A. Thus, VPN gateway 402B, firewall 404B, and load balancer406B may perform the same or similar operations as VPN gateway 402A,firewall 404A, and load balancer 406A, respectively. Further, by way ofreal-time or near-real-time database replication and/or otheroperations, computational instance 322 may exist simultaneously in datacenters 400A and 400B.

Data centers 400A and 400B as shown in FIG. 4 may facilitate redundancyand high availability. In the configuration of FIG. 4, data center 400Ais active and data center 400B is passive. Thus, data center 400A isserving all traffic to and from managed network 300, while the versionof computational instance 322 in data center 400B is being updated innear-real-time. Other configurations, such as one in which both datacenters are active, may be supported.

Should data center 400A fail in some fashion or otherwise becomeunavailable to users, data center 400B can take over as the active datacenter. For example, domain name system (DNS) servers that associate adomain name of computational instance 322 with one or more InternetProtocol (IP) addresses of data center 400A may re-associate the domainname with one or more IP addresses of data center 400B. After thisre-association completes (which may take less than one second or severalseconds), users may access computational instance 322 by way of datacenter 400B.

FIG. 4 also illustrates a possible configuration of managed network 300.As noted above, proxy servers 312 and user 414 may access computationalinstance 322 through firewall 310. Proxy servers 312 may also accessconfiguration items 410. In FIG. 4, configuration items 410 may refer toany or all of client devices 302, server devices 304, routers 306, andvirtual machines 308, any applications or services executing thereon, aswell as relationships between devices, applications, and services. Thus,the term “configuration items” may be shorthand for any physical orvirtual device, or any application or service remotely discoverable ormanaged by computational instance 322, or relationships betweendiscovered devices, applications, and services. Configuration items maybe represented in a configuration management database (CMDB) ofcomputational instance 322.

As noted above, VPN gateway 412 may provide a dedicated VPN to VPNgateway 402A. Such a VPN may be helpful when there is a significantamount of traffic between managed network 300 and computational instance322, or security policies otherwise suggest or require use of a VPNbetween these sites. In some embodiments, any device in managed network300 and/or computational instance 322 that directly communicates via theVPN is assigned a public IP address. Other devices in managed network300 and/or computational instance 322 may be assigned private IPaddresses (e.g., IP addresses selected from the 10.0.0.0-10.255.255.255or 192.168.0.0-192.168.255.255 ranges, represented in shorthand assubnets 10.0.0.0/8 and 192.168.0.0/16, respectively).

IV. EXAMPLE DEVICE, APPLICATION, AND SERVICE DISCOVERY

In order for remote network management platform 320 to administer thedevices, applications, and services of managed network 300, remotenetwork management platform 320 may first determine what devices arepresent in managed network 300, the configurations and operationalstatuses of these devices, and the applications and services provided bythe devices, and well as the relationships between discovered devices,applications, and services. As noted above, each device, application,service, and relationship may be referred to as a configuration item.The process of defining configuration items within managed network 300is referred to as discovery, and may be facilitated at least in part byproxy servers 312.

For purpose of the embodiments herein, an “application” may refer to oneor more processes, threads, programs, client modules, server modules, orany other software that executes on a device or group of devices. A“service” may refer to a high-level capability provided by multipleapplications executing on one or more devices working in conjunctionwith one another. For example, a high-level web service may involvemultiple web application server threads executing on one device andaccessing information from a database application that executes onanother device.

FIG. 5A provides a logical depiction of how configuration items can bediscovered, as well as how information related to discoveredconfiguration items can be stored. For sake of simplicity, remotenetwork management platform 320, third-party networks 340, and Internet350 are not shown.

In FIG. 5A, CMDB 500 and task list 502 are stored within computationalinstance 322. Computational instance 322 may transmit discovery commandsto proxy servers 312. In response, proxy servers 312 may transmit probesto various devices, applications, and services in managed network 300.These devices, applications, and services may transmit responses toproxy servers 312, and proxy servers 312 may then provide informationregarding discovered configuration items to CMDB 500 for storagetherein. Configuration items stored in CMDB 500 represent theenvironment of managed network 300.

Task list 502 represents a list of activities that proxy servers 312 areto perform on behalf of computational instance 322. As discovery takesplace, task list 502 is populated. Proxy servers 312 repeatedly querytask list 502, obtain the next task therein, and perform this task untiltask list 502 is empty or another stopping condition has been reached.

To facilitate discovery, proxy servers 312 may be configured withinformation regarding one or more subnets in managed network 300 thatare reachable by way of proxy servers 312. For instance, proxy servers312 may be given the IP address range 192.168.0/24 as a subnet. Then,computational instance 322 may store this information in CMDB 500 andplace tasks in task list 502 for discovery of devices at each of theseaddresses.

FIG. 5A also depicts devices, applications, and services in managednetwork 300 as configuration items 504, 506, 508, 510, and 512. As notedabove, these configuration items represent a set of physical and/orvirtual devices (e.g., client devices, server devices, routers, orvirtual machines), applications executing thereon (e.g., web servers,email servers, databases, or storage arrays), relationshipstherebetween, as well as services that involve multiple individualconfiguration items.

Placing the tasks in task list 502 may trigger or otherwise cause proxyservers 312 to begin discovery. Alternatively or additionally, discoverymay be manually triggered or automatically triggered based on triggeringevents (e.g., discovery may automatically begin once per day at aparticular time).

In general, discovery may proceed in four logical phases: scanning,classification, identification, and exploration. Each phase of discoveryinvolves various types of probe messages being transmitted by proxyservers 312 to one or more devices in managed network 300. The responsesto these probes may be received and processed by proxy servers 312, andrepresentations thereof may be transmitted to CMDB 500. Thus, each phasecan result in more configuration items being discovered and stored inCMDB 500.

In the scanning phase, proxy servers 312 may probe each IP address inthe specified range of IP addresses for open Transmission ControlProtocol (TCP) and/or User Datagram Protocol (UDP) ports to determinethe general type of device. The presence of such open ports at an IPaddress may indicate that a particular application is operating on thedevice that is assigned the IP address, which in turn may identify theoperating system used by the device. For example, if TCP port 135 isopen, then the device is likely executing a WINDOWS® operating system.Similarly, if TCP port 22 is open, then the device is likely executing aUNIX® operating system, such as LINUX®. If UDP port 161 is open, thenthe device may be able to be further identified through the SimpleNetwork Management Protocol (SNMP). Other possibilities exist. Once thepresence of a device at a particular IP address and its open ports havebeen discovered, these configuration items are saved in CMDB 500.

In the classification phase, proxy servers 312 may further probe eachdiscovered device to determine the version of its operating system. Theprobes used for a particular device are based on information gatheredabout the devices during the scanning phase. For example, if a device isfound with TCP port 22 open, a set of UNIX®-specific probes may be used.Likewise, if a device is found with TCP port 135 open, a set ofWINDOWS®-specific probes may be used. For either case, an appropriateset of tasks may be placed in task list 502 for proxy servers 312 tocarry out. These tasks may result in proxy servers 312 logging on, orotherwise accessing information from the particular device. Forinstance, if TCP port 22 is open, proxy servers 312 may be instructed toinitiate a Secure Shell (SSH) connection to the particular device andobtain information about the operating system thereon from particularlocations in the file system. Based on this information, the operatingsystem may be determined. As an example, a UNIX® device with TCP port 22open may be classified as AIX®, HPUX, LINUX®, MACOS®, or SOLARIS®. Thisclassification information may be stored as one or more configurationitems in CMDB 500.

In the identification phase, proxy servers 312 may determine specificdetails about a classified device. The probes used during this phase maybe based on information gathered about the particular devices during theclassification phase. For example, if a device was classified as LINUX®,a set of LINUX®-specific probes may be used. Likewise, if a device wasclassified as WINDOWS® 2012, as a set of WINDOWS®-2012-specific probesmay be used. As was the case for the classification phase, anappropriate set of tasks may be placed in task list 502 for proxyservers 312 to carry out. These tasks may result in proxy servers 312reading information from the particular device, such as basicinput/output system (BIOS) information, serial numbers, networkinterface information, media access control address(es) assigned tothese network interface(s), IP address(es) used by the particular deviceand so on. This identification information may be stored as one or moreconfiguration items in CMDB 500.

In the exploration phase, proxy servers 312 may determine furtherdetails about the operational state of a classified device. The probesused during this phase may be based on information gathered about theparticular devices during the classification phase and/or theidentification phase. Again, an appropriate set of tasks may be placedin task list 502 for proxy servers 312 to carry out. These tasks mayresult in proxy servers 312 reading additional information from theparticular device, such as processor information, memory information,lists of running processes (applications), and so on. Once more, thediscovered information may be stored as one or more configuration itemsin CMDB 500.

Running discovery on a network device, such as a router, may utilizeSNMP. Instead of or in addition to determining a list of runningprocesses or other application-related information, discovery maydetermine additional subnets known to the router and the operationalstate of the router's network interfaces (e.g., active, inactive, queuelength, number of packets dropped, etc.). The IP addresses of theadditional subnets may be candidates for further discovery procedures.Thus, discovery may progress iteratively or recursively.

Once discovery completes, a snapshot representation of each discovereddevice, application, and service is available in CMDB 500. For example,after discovery, operating system version, hardware configuration andnetwork configuration details for client devices, server devices, androuters in managed network 300, as well as applications executingthereon, may be stored. This collected information may be presented to auser in various ways to allow the user to view the hardware compositionand operational status of devices, as well as the characteristics ofservices that span multiple devices and applications.

Furthermore, CMDB 500 may include entries regarding dependencies andrelationships between configuration items. More specifically, anapplication that is executing on a particular server device, as well asthe services that rely on this application, may be represented as suchin CMDB 500. For instance, suppose that a database application isexecuting on a server device, and that this database application is usedby a new employee onboarding service as well as a payroll service. Thus,if the server device is taken out of operation for maintenance, it isclear that the employee onboarding service and payroll service will beimpacted. Likewise, the dependencies and relationships betweenconfiguration items may be able to represent the services impacted whena particular router fails.

In general, dependencies and relationships between configuration itemsmay be displayed on a web-based interface and represented in ahierarchical fashion. Thus, adding, changing, or removing suchdependencies and relationships may be accomplished by way of thisinterface.

Furthermore, users from managed network 300 may develop workflows thatallow certain coordinated activities to take place across multiplediscovered devices. For instance, an IT workflow might allow the user tochange the common administrator password to all discovered LINUX®devices in a single operation.

In order for discovery to take place in the manner described above,proxy servers 312, CMDB 500, and/or one or more credential stores may beconfigured with credentials for one or more of the devices to bediscovered. Credentials may include any type of information needed inorder to access the devices. These may include userid/password pairs,certificates, and so on. In some embodiments, these credentials may bestored in encrypted fields of CMDB 500. Proxy servers 312 may containthe decryption key for the credentials so that proxy servers 312 can usethese credentials to log on to or otherwise access devices beingdiscovered.

The discovery process is depicted as a flow chart in FIG. 5B. At block520, the task list in the computational instance is populated, forinstance, with a range of IP addresses. At block 522, the scanning phasetakes place. Thus, the proxy servers probe the IP addresses for devicesusing these IP addresses, and attempt to determine the operating systemsthat are executing on these devices. At block 524, the classificationphase takes place. The proxy servers attempt to determine the operatingsystem version of the discovered devices. At block 526, theidentification phase takes place. The proxy servers attempt to determinethe hardware and/or software configuration of the discovered devices. Atblock 528, the exploration phase takes place. The proxy servers attemptto determine the operational state and applications executing on thediscovered devices. At block 530, further editing of the configurationitems representing the discovered devices and applications may takeplace. This editing may be automated and/or manual in nature.

The blocks represented in FIG. 5B are for purpose of example. Discoverymay be a highly configurable procedure that can have more or fewerphases, and the operations of each phase may vary. In some cases, one ormore phases may be customized, or may otherwise deviate from theexemplary descriptions above.

V. NATURAL LANGUAGE PROCESSING OF TEXT QUERIES

Natural language processing is a discipline that involves, among otheractivities, using computers to understand the structure and meaning ofhuman language. This determined structure and meaning may be applicableto the processing of IT incidents, as described below.

Each incident may be represented as an incident report. While incidentreports may exist in various formats and contain various types ofinformation, an example incident report 600 is shown in FIG. 6. Incidentreport 600 consists of a number of fields in the left column, at leastsome of which are associated with values in the right column.

Field 602 identifies the originator of the incident, in this case BobSmith. Field 604 identifies the time at which the incident was created,in this case 9:56 AM on Feb. 7, 2018. Field 605 is a text string thatprovides a short description of the problem. Field 606 identifies thedescription of the problem, as provided by the originator. Thus, field606 may be a free-form text string containing anywhere from a few wordsto several sentences or more. Field 608 is a categorization of theincident, in this case email. This categorization may be provided by theoriginator, the IT personnel to whom the incident is assigned, orautomatically based on the context of the problem description field.

Field 610 identifies the IT personnel to whom the incident is assigned(if applicable), in this case Alice Jones. Field 612 identifies thestatus of the incident. The status may be one of “open,” “assigned,”“working,” or “resolved” for instance. Field 614 identifies how theincident was resolved (if applicable). This field may be filled out bythe IT personnel to whom the incident is assigned or another individual.Field 616 identifies the time at which the incident was resolved, inthis case 10:10 AM on Feb. 7, 2018. Field 618 specifies the closure codeof the incident (if applicable) and can take on values such as “closed(permanently)”, “closed (work around)”, “closed (cannot reproduce)”,etc. Field 620 identifies any additional notes added to the record, suchas by the IT personnel to whom the incident is assigned. Field 622identifies a link to an online article that may help users avoid havingto address a similar issue in the future.

Incident report 600 is presented for purpose of example. Other types ofincident reports may be used, and these reports may contain more, fewer,and/or different fields.

Incident reports, such as incident report 600, may be created in variousways. For instance, by way of a web form, an email sent to a designatedaddress, a voicemail box using speech-to-text conversion, and so on.These incident reports may be stored in an incident report database thatcan be queried. As an example, a query in the form of a text stringcould return one or more incident reports that contain the words in thetext string. Additionally or alternatively, one or more elements of anincident report (e.g., a “short description” field) may be used to querya database of knowledgebase articles, other incident reports, or someother corpus of text. This may be done in order to identify otherincident reports, resolved past incident reports, reports on theresolution of past problems, knowledgebase articles, or otherinformation that may be relevant to the incident report in order tofacilitate resolution of a problem represented in the incident report.

This process is illustrated in FIG. 7. A text query may be entered intoweb interface 700. This web interface may be supplied by way of acomputational instance of remote network management platform 320. Webinterface 700 converts the text query into a database query (e.g., anSQL query), and provides the SQL query to database 702. This databasemay be CMDB 500 or some other database. Database 702 contains a numberof incident reports with problem description fields as shown in FIG. 6.Regardless, database 702 conducts the query and returns matching resultsto web interface 700. One or more such results may be returned. Webinterface 700 provides these results as a web page.

For example, if the text query is “email”, web interface 700 may convertthis query into an SQL query of database 702. For example, the query maylook at the problem description field of a table containing incidentreports. Any such incident report that matches the query—i.e., includesthe term “email”—may be provided in the query results. Thus, theincident reports with the problem descriptions of “My email client isnot downloading new emails”, “Email crashed”, and “Can't connect toemail” may be provided, while the incident report with the problemdescription “VPN timed out” is not returned.

This matching technique is simplistic and has a number of drawbacks. Itonly considers the presence of the text of the query in the incidents.Thus, it does not consider contextual information, such as wordsappearing before and after the query text. Also, synonyms of the querytext (e.g., “mail” or “message”) and misspellings of the query text(e.g., “emial”) would not return any results in this example.

Furthermore, deploying such a solution would involve use of aninefficient sparse matrix, with entries in one dimension for each wordin the English language and entries in the other dimension for theproblem description of each incident. While the exact number of Englishwords is a matter of debate, there are at least 150,000-200,000, withless than about 20,000 in common use. Given that a busy IT departmentcan have a database of tens of thousands of incidents, this matrix wouldbe quite large and wasteful to store even if just the 20,000 mostcommonly used words are included.

Thus, the above methods of comparison may be replaced by and/oraugmented with a variety of methods that compare the semantic contentand/or context of text samples. These methods can improve a variety ofmachine learning techniques to facilitate natural language processing.Such techniques can include determining word and/or paragraph vectorsfrom samples of text, applying artificial neural networks or other deeplearning algorithms, sentiment analysis, or other techniques in order todetermine a similarity between samples of text. For example, these orother natural language processing techniques can be applied to determinethe similarity between one or more text fields of an incident report andother incident reports, resolved incident reports, knowledgebasearticles, or other potentially relevant samples of text.

However, particular segments of text within a large sample of text mayvary with respect to similarity or relevance to a query text sample. Forexample, a particular article may include segment(s) that are highlyrelevant to a particular query text, while the remainder of the articlehas a similarity to the query text that is much lower. In such examples,determining a similarity value for the article as a whole may result indiscarding articles that contain relevant sub-sections. Additionally,providing a user with a particular article or other large sample of textthat is overall “relevant” may result in lost time as the user perusesthe entire article to find a particular sub-section of relevance totheir problem. Further, it can be beneficial to compare text queries tosections of text that are similar with respect to size to the textqueries.

It can therefore be beneficial to partition knowledgebase articles orother large samples of text into multiple segments. The similaritybetween a query text and each of the segments of the large text can thenbe determined and used to determine whether the large text is relevantto the query text. This could include using the similarity values foreach of the segments of the large text to determine an overallsimilarity value for the large text. Additionally or alternatively, thesegments of the large text could be treated as independent text samples.In such an example, a “most relevant” or otherwise most similar segmentto a query text could be selected from the set of segments of the textsamples, rather than restricting the selection to full text samples.

Determining similarity and/or relevance at the segment level can allowfor finer-grained detection of relevant portions of text within a corpusof text samples, preventing relevant portions of larger text samplesfrom being discarded due to making up a relatively small proportion ofthe large text sample that they are a part of. Further, comparing textqueries to text segments having similar size (e.g., number of words,number of clauses, number of sentences) to the text queries may permitmore suitable comparisons to be made than comparisons between the textqueries and un-segmented samples of text that have sizes that are muchlarger than the text queries. Additionally, these methods can allow thedetermination of the overall similarity of large samples of text to betailored to a particular application. For example, to be more thoroughin detecting every relevant portion of text within a corpus of textsamples, the similarity value of a particular text sample could bedetermined as the maximum of the similarity values determined forsegments of text within the particular text sample. In another example,articles that are more “on-topic” overall could be selected-for bydetermining, for each article, an average of the similarity values forsegments of text within each article so that articles that only includea single ‘similar’ segment are disfavored relative to articles thatinclude multiple ‘similar’ segments.

FIG. 8 depicts an example query text sample 810, a first large textsample 820 and a second large text sample 830. The first large textsample 820 is divided into three segments of text 820A, 820B, 820C andthe second large text sample 830 is divided into three segments of text830A, 830B, 830C. The query text sample 810 could be a sample of textfrom an incident report. For example, the query text sample 810 could bethe problem description field 606 from the incident report 600 depictedin FIG. 6. The large text samples could be articles in a knowledgebaseor other database or fields of other incident reports.

The segments within such samples of text could be determined in avariety of ways. In some examples, the segments could be generatedmanually, for example, by the author of knowledgebase articlesannotating the articles in order to indicate conceptually discretesegments of the article. Additionally or alternatively, the segmentscould be automatically generated. This could include determining thesegments within a sample of text based on line breaks, punctuation,headers, or other information. A machine learning algorithm could betrained to identify segments within samples of text, e.g., based on atraining set of query texts.

As shown in FIG. 8, the segments of text could be non-overlapping andcould each represent sets of discrete sentences. However, FIG. 8 isintended as a non-limiting example of segments within samples of text,and other types of segmentation of text are anticipated. For example,the segments of text could include portions of sentences. That is, asegment of text could end and/or begin in the middle of a sentence. Insome examples, the segments of text within a particular large sample oftext could overlap. Such overlap could allow a transitional portion ofthe text to be associated with both a preceding portion of the text andwith a subsequent portion of the text.

As noted above, similarity values could be determined between a querytext and each segment within each sample of text in a corpus of textsamples (e.g., between one or more fields of an incident report and acorpus of articles describing the resolution of past incident reports).These similarity values could then be used to select and provide to auser those segments that are most similar to the query text. Thesegments could be provided on their own or as part of the articles ofwhich they are a part (e.g., with the selected segment(s) highlighted orotherwise indicated within the article).

Additionally or alternatively, the set of similarity values determinedfor the segments of a text sample could be used to determine an overallsimilarity value for the text sample. The overall similarity valuescould then be used to select and provide to a user those text samplesthat are most similar to the query text. The method used to generate theoverall similarity value from the segment similarity values could beselected in order to affect the sorts of text samples that are selected.

In some examples, the overall similarity value for a text sample couldbe determined as the maximum of the similarity values of the textsegments within the text sample. This method could be used in order toidentify highly relevant segments of text even when those segments arepart of text samples that are otherwise less relevant.

In some examples, a sum of the similarity values of the text segmentscould be determined as the overall similarity value of the text sample.This method could be used in order to identify text samples that include‘more’ segments of text that are ‘more’ relevant to the query text. Thesum could be normalized to the number of segments in the text sample(i.e., the overall similarity value could be an average of the segmentsimilarity values) in order to emphasize text samples that are more ‘ontopic’ with respect to content that is relevant to the query text.

In some examples, a weighted sum of some or all of the segmentsimilarity values could be determined as the overall similarity value ofthe text sample. For example, the overall similarity value could bedetermined as a sum of 100% of the similarity value for the most similarsegment, 80% of the similarity value for the second most similarsegment, 60% of the similarity value for the third most similar segment,etc. In another example, an iterative method could be applied to add aweighted version of the similarity of each segment together. An exampleof this iterative method, for similarity values between 0 and 1inclusive, can be represented as:

sort the segment similarity values from high to low

overall similarity value←highest segment score

for the remainder of the segments [i]=2 to end:

-   -   if the similarity value for segment[i]>=overall similarity        value/ρ overall similarity value←overall similarity        value+(1−overall similarity value)*similarity value for        segment[i]

The parameter ρ can be beneficially set to a value between 1 and 2,inclusive. If a value of 1 is chosen, only the highest similarity valuewill be considered. The weighting parameter ρ is provided so that textsamples with multiple low-value segments do not end up with a higheroverall score than a text sample that has a single segment with a highsimilarity value. Additionally, this method ensures that the overallsimilarity value is not greater than 1, no matter the number of segmentswithin a particular text sample.

For These methods could be used in order to identify text samples thathave a few ‘highly relevant’ segments while also identifying textsamples that include multiple segments that are individually lessrelevant but that may, in aggregate, be relevant to the query text. Thenumber of segment similarity values used to generate the weighted sumcould be limited, e.g., to the top three segments. This could be done inorder to emphasize text samples that include fewer segments with highersimilarity scores relative to text samples that have larger number ofrelatively less relevant segments.

In some examples, the text segment similarity could be compared to athreshold in order to determine an overall similarity value for a textsample. For example, only segment similarity values that exceed athreshold similarity value could be summed together to generate anoverall text sample similarity value. In another example, the number ofsegment similarity values that exceed the threshold similarity valuecould be determined as the overall text sample similarity value. Suchthreshold-comparison methods could be used in order to base theassessment of the overall relevance of the text samples only on thosesegments that are likely to be actually relevant to the query text. Theindividual segments' similarity values being greater than the thresholdlevel can be used as a proxy for such a likelihood. The count ofsupra-threshold similarity values could be normalized to the number ofsegments in the text sample in order to emphasize those text samplesthat are fractionally more directed toward content that is relevant tothe query text.

VI. NATURAL LANGUAGE PROCESSING OF TEXT QUERIES BASED ON SEMANTICCONTENT

The degree of similarity between two samples of text can be determinedin a variety of ways. The two samples of text could be a text field ofan incident report and a text field of another incident report, a textfield of a resolved incident report, a knowledgebase article, or someother sample of text that may be relevant to the resolution,classification, or other aspects of an incident report. Additionally oralternatively, one or both of the samples could be segments of textwithin a larger sample of text. As noted above, a degree of overlapbetween the identities of words present in the two samples of textand/or a word matrix method could be used to determine the degree ofsimilarity. Additionally or alternatively, one or more techniques ofnatural language processing could be applied to compare the samples oftext such that the context or other semantic content of the textsaffects the determined similarity value between the samples of text.

Such techniques may be applied to improve text query matching related toincident reports. These techniques may include a variety of machinelearning algorithms that can be trained based on samples of text. Thesamples of text used for training can include past examples of incidentreports, knowledgebase articles, or other text samples of the samenature as the text samples to which the trained model will be applied.This has the benefit of providing a model that has been uniquely adaptedto the vocabulary, topics, and idiomatic word use common in its intendedapplication.

Such techniques can include determining word and/or paragraph vectorsfrom samples of text, applying ANNs or other deep learning algorithms,performing sentiment analysis, or other techniques in order to determinea similarity between samples of text, to group multiple samples of texttogether according to topic or content, to partition a sample of textinto discrete internally-related segments, to determine statisticalassociations between words, or to perform some other language processingtask. Below, a particular method for determining similarity valuesbetween samples of text using an ANN model that provides compactsemantic representations of words and text strings is provided as anon-limiting example of such techniques. However, other techniques maybe applied to generate similarity values between samples of text asapplied elsewhere herein. In the discussion below, there are twoapproaches for training an ANN model to represent the sematic meaningsof words: word vectors and paragraph vectors. These techniques may becombined with one another or with other techniques.

These techniques may also be applied to partition segments of text intomultiple text segments. Such partitioning can allow for morefine-grained detection of similarity between query texts (e.g., aproblem description field of an incident report) and larger samples oftext. This is because a similarity value can be determined between thequery text and each of the segments of the large sample of text. As aresult, the relevance of samples of text that are largely unrelated tothe query text, but that include a subsection of high relevance to thequery text, may be detected.

A. Word Vectors

A “word vector” may be determined for each word present in a corpus oftext records such that words having similar meanings (or “semanticcontent”) are associated with word vectors that are near each otherwithin a semantically encoded vector space. Such vectors may havedozens, hundreds, or more elements. These word vectors allow theunderlying meaning of words to be compared or otherwise operated on by acomputing device. Accordingly, the use of word vectors may allow for asignificant improvement over simpler word list or word matrix methods.

Word vectors can be used to quickly and efficiently compare the overallsemantic content of samples of text, allowing a similarity value betweenthe samples of text to be determined. This can include determining adistance, a cosine similarity, or some other measure of similaritybetween the word vectors of the words in each of the text samples. Forexample, a mean of the word vectors in each of the text samples could bedetermined and a cosine similarity between the means then used as ameasure of similarity between the text samples. Additionally oralternatively, the word vectors may be provided as input to an ANN, asupport vector machine, a decision tree, or some other machine learningalgorithm in order to perform sentiment analysis, to classify or clustersamples of text, to determine a level of similarity between samples oftext, or to perform some other language processing task.

Word vectors may be determined for a set of words in a variety of ways.In an example, a matrix of the word vectors can be an input layer of anANN. The ANN (including the matrix of word vectors) can then be trainedwith a large number of text strings from a database to determine thecontextual relationships between words appearing in these text strings.Such an ANN 900 is shown in FIG. 9A. ANN 900 includes input layer 902,which feeds into hidden layer 904, which in turn feeds into output layer906. The number of nodes in input layer 902 and output layer 906 may beequivalent to the number of words in a pre-defined vocabulary ordictionary (e.g., 20,000, 50,000, or 100,000). The number of nodes inhidden layer 904 may be much smaller (e.g., 64 as shown in FIG. 9A, orother values such as 16, 32, 128, 512, 1024, etc.).

For each text string in the database, ANN 900 is trained with one ormore arrangements of words. For instance, in FIG. 9B, ANN 900 is shownbeing trained with input word “email” and output (context) words“can't”, “connect” and “to”. The output words serve as the ground truthoutput values to which the results produced by output layer 906 arecompared. This arrangement reflects that “email” appears proximate to“can't”, “connect” and “to” in a text string in database 702.

In an implementation, this could be represented as node I₂ receiving aninput of 1, and all other nodes in input layer 902 receiving an input of0. Similarly, node O₁ is associated with a ground truth value of“can't”, node O₂ is associated with a ground truth value of “connect”,and node O₃ is associated with a ground truth value of “to”. In theimplementation, this could be represented as nodes O₁, O₂, and O₃ beingassociated with ground truth values of 1 and all other nodes in outputlayer 906 being associated with ground truth values of 0. The lossfunction may be a sum of squared errors, for example, between theoutputs generated by output layer 906 in response to the input describedabove and a vector containing the ground truth values associated withthe output layer nodes.

Other arrangements of this text string from database 702 may be used totrain ANN 900. For instance, as shown in FIG. 9C, the input word may be“can't” and the output words may be “connect”, “to”, and “email.” Inanother example, as shown in FIG. 9D, the input word may be “connect”and the output words may be “can't”, “to”, and “email.”

In general, these arrangements may be selected so that the output wordsare within w words of the input word (e.g., where w could be 1, 2, 3, 5,etc.), the output words are in the same sentence as the input word, theoutput words are in the same paragraph as the input word, and so on.Furthermore, various word arrangements of each text string in database702 may be used to train ANN 900. These text strings may be selectedfrom short description field 605, problem description field 606,category field 608, resolution field 614, notes field 620, and/or anyother field or combination of fields in an incident report.

After ANN 900 is trained with these arrangements of text strings, hiddenlayer 904 becomes a compact vector representation of the context andmeaning of an input word. That is, the weightings from a particular node(e.g., I₃) in the input layer 902 to the hidden layer 904 represent theelements of the word vector of the word corresponding to the particularnode (e.g., “can't”). For example, assuming that ANN 900 isfully-trained with a corpus of 10,000 or so text strings (though more orfewer text strings may be used), an input word of “email” may have asimilar vector representation of an input word of “mail”. Intuitively,since hidden layer 904 is all that ANN 900 has to determine the contextof an input word, if two words have similar contexts, then they arehighly likely to have similar vector representations.

In some embodiments, ANN 900 can be trained with input words associatedwith the output nodes O₁ . . . O_(n) and the output (context) wordsassociated with input nodes I_(n). This arrangement may produce anidentical or similar vector for hidden layer 904.

Furthermore, vectors generated in this fashion are additive. Thus,subtracting the vector representation of “mail” from the vectorrepresentation of “email” is expected to produce a vector with valuesclose to 0. However, subtracting the vector representation of “VPN” fromthe vector representation of “email” is expected to produce a vectorwith higher values. In this manner, the model indicates that “email” and“mail” have closer meanings than “email” and “VPN”.

Once vector representations have been determined for all words ofinterest, linear and/or multiplicative aggregations of these vectors maybe used to represent text strings. For instance, a vector for the textstring “can't connect to email” can be found by adding together theindividual vectors for the words “can't”, “connect”, “to”, and “email”.In some cases, an average or some other operation may be applied to thevectors for the words. This can be expressed below as the vector sum ofm vectors v_(i) with each entry therein divided by m, where i={1 . . .m}. But other possibilities, such as weighted averages, exist.

$\begin{matrix}{v_{avg} = {\frac{1}{m}{\sum\limits_{i = 1}^{m}v_{i}}}} & (1)\end{matrix}$

Regardless of how the aggregations are determined, this generaltechnique allows vector representations for each text string in database702 to be found. These vector representations may be stored in database702 as well, either along with their associated text strings orseparately. These vector representations can then be used to compare thetext strings, cluster or group the text strings, train some othermachine learning classifier, or to perform some other task. For example,a matching text string for a particularly query text may be determinedby determining a cosine similarity or other similarity value between thevector representation of the query text and the stored vectorrepresentations of samples of text in the database 702.

The comparison may identify one or more text string vectors fromdatabase 702 that “match” in this fashion. In some cases this may be thek text string vectors with the highest similarity, or any text stringvector with a similarity that is greater than a pre-determined value.The identified text string vectors could correspond to a subset ofincident reports, within a greater corpus of incident reports that isrecorded in the database 702, that are relevant to an additionalincident report that corresponds to the query text string vector. Foreach of the identified text string vectors, the associated text stringmay be looked up in database 702 and provided as an output text string.In some cases, the associated incident reports may be provided as well.

In some cases, only incident reports that are not older than apre-determined age are provided. For instance, the system may beconfigured to identify text string vectors only from incident reportsthat were resolved within the last 3 months, 6 months, or 12 months.Alternatively, the system may be configured to identify text stringvectors only from incident reports that were opened within the last 3months, 6 months, or 12 months.

In this fashion, incident reports with similar problem descriptions asthat of the input text string can be rapidly identified. Notably, thissystem provides contextual results that are more likely to be relevantand meaningful to the input text string. Consequently, an individual canreview these incident reports to determine how similar problems as thatin the problem description have been reported and addressed in the past.This may result in the amount of time it takes to resolve incidentsbeing dramatically reduced.

Additionally or alternatively, these embodiments can be applied todetect and identify clusters of semantically and/or contextually similarincident reports within a corpus of incident reports. For example,clusters of incident reports related to a similar issue that is likelyto affect users of an IT system, an ongoing misconfiguration of one ormore aspects of an IT system, a progressive hardware failure in acomponent of an IT system, or some other recurring issue within an ITsystem. Identifying such clusters of related incident reports can allowthe IT system to be repaired or upgraded (e.g., by replacing and/orreconfiguring failing or inconsistently performing hardware orsoftware), users to be trained to avoid common mistakes,rarely-occurring hardware or software issues to be detected andrectified, or other benefits.

Such clusters of relevant incident reports can be detected and/oridentified by identifying, within the semantically encoded vector space,aggregated word (and/or paragraph) vectors corresponding to the incidentreports. A variety of methods could be employed to detect such clusterswithin the semantically encoded vector space, e.g., k-means clustering,support vector machines, ANNs (e.g., unsupervised ANNs configured and/ortrained to identify relevant subsets of training examples within acorpus of available training examples), or some other classifier orother method for identifying clusters of related vectors within a vectorspace.

B. Paragraph Vectors

As discussed previously, ANN model 900 uses the surrounding context toprovide compact, semantically relevant vector representations of words.After training, words with similar meanings can map to a similarposition in the vector space. For example, the vectors for “powerful”and “strong” may appear close to each other, whereas the vectors for“powerful” and “Paris” may be farther apart. Additions and subtractionsbetween word vectors also carry meaning. Using vector algebra on thedetermined word vectors, we can answer analogy questions such as“King”−“man”+“woman”=“Queen.”

However, the complete semantic meaning of a sentence or other passage(e.g., a phrase, several sentences, a paragraph, a text segment within alarger sample of text, or a document) cannot always be captured from theindividual word vectors of a sentence (e.g., by applying vectoralgebra). Word vectors can represent the semantic content of individualwords and may be trained using short context windows. Thus, the semanticcontent of word order and any information outside the short contextwindow is lost when operating based only on word vectors.

Take for example the sentence “I want a big green cell right now.” Inthis case, simple vector algebra of the individual words may fail toprovide the correct semantic meaning of the word “cell,” as the word“cell” has multiple possible meanings and thus can be ambiguous.Depending on the context, “cell” could be a biological cell, a prisoncell, or a cell of a cellular communications network. Accordingly, theparagraph, sentence, or phrase from which a given word is sampled canprovide crucial contextual information.

In another example, given the sentence “Where art thou ______,” it iseasy to predict the missing word as “Romeo” if sentence was said toderive from a paragraph about Shakespeare. Thus, learning a semanticvector representation of an entire paragraph can help contribute topredicting the context of words sampled from that paragraph.

Similar to the methods above for learning word vectors, an ANN or othermachine learning structures may be trained using a large number ofparagraphs in a corpus to determine the contextual meaning of entireparagraphs, sentences, phrases, or other multi-word text samples as wellas to determine the meaning of the individual words that make up theparagraphs in the corpus. Such an ANN 1000 is shown in FIG. 10A. ANN1000 includes input layer 1002, which feeds into hidden layer 1004,which in turn feeds into output layer 1006. Note that input layer 1002consists of two types of input substructures, the top substructure 1008(consisting of input nodes I₁ . . . I_(n)) representing words and thebottom substructure 1010 (consisting of input nodes D₁ . . . D_(m))representing paragraphs (documents). The number of nodes in output layer1006 and the top input layer substructure 1008 may be equal to thenumber of unique words in the entire corpus. The number of nodes in thebottom input layer substructure 1010 may be equivalent to the number ofunique paragraphs in the entire corpus. Note that “paragraph,” as usedherein, may be a sentence, a paragraph, one or more fields of anincident report, a segment of a larger string of text, or some othermulti-word string of text.

For each paragraph in the corpus, ANN 1000 is trained with fixed-lengthcontexts generated from moving a sliding window over the paragraph.Thus, a given paragraph vector is shared across all training contextscreated from its source paragraph, but not across training contextscreated from other paragraphs. Word vectors are shared across trainingcontexts created from all paragraphs, e.g., the vector for “cannot” isthe same for all paragraphs. Paragraphs are not limited in size; theycan be as large as entire documents or as small as a sentence or phrase.In FIG. 10A, ANN 1000 is shown in a single training iteration, beingtrained with input word context “can't,” “connect” and “to,” inputparagraph context DOC 1, and output word “email.” The output word servesas the ground truth output value to which the result produced by outputlayer 1006 is compared. This arrangement reflects that “email” appearsproximate to “can't”, “connect”, and “to”, and is within DOC 1.

In an implementation, this could be represented as output node O₄receiving a ground truth value of 1 and all other nodes in output layer1006 having ground truth values of 0. Similarly, node I_(i) has a groundtruth value of “can't,” node I₂ has a ground truth value of “connect,”node I₃ has a ground truth value of “to,” and node D₁ has ground truthvalue of DOC 1. In the implementation, this could be represented asnodes I₁, I₁, I₃, and D₁ being associated with values of 1 and all othernodes in input layer 1002 having values of 0. The loss function may be asum of squared errors, for example, between the output of output layer1006 and a vector containing the ground truth values. The weight valuesof the corresponding word vectors and paragraph vectors, as well all theoutput layer parameters (e.g., softmax weights) are updated based on theloss function (e.g., via backpropagation).

FIG. 10B shows ANN 1000 being trained with a subsequent context window.This context window derives from the same document, but shifts ahead aword in the document and uses input word context “connect,” “to” and“email,” input paragraph context DOC 1, and output word “server.” In animplementation, these inputs and outputs can be encoded with groundtruth values as similarly described above.

FIG. 10C shows an instance of ANN 1000 trained with another documentwithin the corpus. The context window derives from this document anduses input word context “can't”, “load”, and “my”, input paragraphcontext DOC 2, and output word “database.” In an implementation, theseinputs and outputs can be encoded with ground truth values as similarlydescribed above.

After ANN 1000 is trained, the weights associated with hidden layer 1004become a compact vector representation of the context and meaning ofinput words and paragraphs. For example, assuming that ANN 1000 isfully-trained with a corpus of 1,000 paragraphs, with the entire corpuscontaining 10,000 unique words, each paragraph and each word can berepresented by a unique vector with a length equal to the number ofhidden nodes in hidden layer 1004. These unique vectors encode thecontextual meaning of words within the paragraphs or the paragraphsthemselves.

FIG. 10D shows ANN 1000 at prediction time performing an inference stepto compute the paragraph vector for a new, previously unseen paragraph.This inference step begins by adding an additional input node 1012 toinput layer substructure 1010 that represents the unseen paragraph (DOCM+1). During this inference process, the coefficients of the wordvectors substructure 1008 and the learned weights between hidden layer1004 and output layer 1006 are held fixed. Thus, the model generates anadditional paragraph vector 1012, corresponding to the unseen paragraphin the input paragraph vector substructure 1010, to obtain the newsemantic vector representation of the unseen paragraph. Any additionalunseen paragraphs can be trained through a similar process by addinginput nodes to input layer substructure 1010.

Alternatively, paragraph vectors can be trained by ignoring word contextin the input layer, only using the paragraph vector as the input, andforcing the model to predict different word contexts randomly sampledfrom the paragraph in the output layer. The input layer of such an ANNonly consists of paragraph vectors, while the output layer represents asingle context window that is randomly generated from a given paragraph.Training such an ANN may result in a vector representation for thesemantic content of paragraphs in the corpus, but will not necessarilyprovide any semantic vector representations for the words therein.

Once vector representations have been determined for paragraphs in thecorpus, linear and/or multiplicative aggregation of these vectors may beused to represent topics of interest. Furthermore, if the dimensions ofparagraph vectors are the same as the dimensions of word vectors, asshown in ANN 1000, then linear and multiplicative aggregation betweenword vectors and paragraphs vectors can be obtained. For example,finding the Chinese equivalent of “Julius Caesar” using an encyclopediaas a corpus can be achieved by vector operations PV(“JuliusCaesar”)−WV(“Roman”)+WV(“Chinese”), where PV is a paragraph vector(representing an entire Wikipedia article) and WV are word vectors.Thus, paragraph vectors can achieve the same kind of analogies to wordvectors with more context-based results.

In practice, such learned paragraph vectors can be used as inputs intoother supervised learning models, such as sentiment prediction models.In such models, which can include but are not limited to ANNs, SupportVector Machines (SVMs), or Naïve Bayes Classifiers, paragraph vectorsare used as input with a corresponding sentiment label as output. Othermetrics such as cosine similarity and nearest neighbors clusteringalgorithms can be applied to paragraph vectors to find or groupparagraphs on similar topics within the corpus of paragraphs.

In the present embodiments, a combination of learned word vectors andparagraph vectors can help determine the structure and meaning ofincidents reports, for example incident report 600 as shown in FIG. 6.Incident report 600 consists of a number of fields in the left column,at least some of which are associated with values in the right column.For longer text fields, such as short description field 605, problemdescription field 606, resolution field 614, and notes field 620, it maybe preferable to represent the associated right column text as aparagraph vector, or as multiple paragraph vectors corresponding torespective text segments within the right column text, to gain morecontextual meaning rather than aggregating the individual word vectorsthat form the text. Incident report 600 is presented for purpose ofexample. Various fields of an incident report can be arranged to berepresented as paragraph vectors, word vectors, or weighted combinationsof the two. Other types of incident reports, problem reports, casefiles, or knowledgebase articles may also be used, and these reports maycontain more, fewer, and/or different fields.

After representing different fields as paragraph vectors, word vectors,or weighted combinations of the two, a single vector to represent theentire incident can be generated by concatenating, generating a vectorsum, or otherwise aggregating the word and/or paragraph vectorrepresentations of the individual incident fields. With a singleaggregate incident vector representation, a system can be configured toidentify similar aggregate vectors (and therefore similar incidentreports) based on cosine similarity or other metrics as discussed above.Alternatively, a search for similar incident reports may use just theparagraph text of one or more individual fields. In this fashion, textfrom one or more individual fields in an incident report could becombined into a single paragraph of text. A paragraph vector could thenbe generated from this single, large paragraph of concatenated text andused to search for similar incidents.

This process can be illustrated in terms of the previously described ANNstructures. Initially, text strings are obtained from database 702 ofFIG. 7. As noted above, these text strings may be from parts of incidentreports. Then, words are extracted from the text strings. The wordsextracted may be all of the words in the text strings or some of thesewords. These extracted words are provided as input to ANN 900 of FIGS.9A-9D. The substring contexts of these words are extracted from the textstrings. The sub string contexts may be one or more substringscontaining words before, after, or surrounding the associated words thatwere extracted. These vector representations may then be used to compare(e.g., using cosine similarity) their respective text samples.

The comparison may identify one or more incident reports from database702 that “match” in this fashion. In some cases this may be the kincident reports with the highest similarity, or any incident reportwith a similarity that is greater than a pre-determined value. The usermay be provided with these identified incident reports or referencesthereto.

In some cases, only incident reports that are not older than apre-determined age are provided. For instance, the system may beconfigured to only identify incident reports that were resolved withinthe last 3 months, 6 months, or 12 months. Alternatively, the system maybe configured to only identify incident reports that were opened withinthe last 3 months, 6 months, or 12 months.

In this fashion, incident reports with similar content as that of theinput incident report can be rapidly identified. Consequently, anindividual can review these incident reports to determine how similarproblems as that in the incident have been reported and addressed in thepast. This may result in the amount of time it takes to resolveincidents being dramatically reduced.

While this section describes some possible embodiments of word vectorsand paragraph vectors, other embodiments may exist. For example,different ANN structures and different training procedures can be used.

C. Partitioning of Text Tamples

As noted above, it can be beneficial to determine similarity between aquery text and individual segments of a comparison text sample, ratherthan or in addition to determining a similarity between the query textand the comparison text sample as a whole. The segments may overlap ormay be non-overlapping. The segments may be limited to containingdiscrete, whole paragraphs or sentences, or may be specified to includeportions of sentences and/or paragraphs.

The segments may be determined manually. For example, a user couldannotate a text sample to indicate the extent of conceptually discreteportions of the text sample. In some examples, this could includeinserting sections headings, with the section heading being later usedas indications of segments within the text sample. Additionally oralternatively, a sample of text could be segmented using an automaticalgorithm.

Such an automatic algorithm could operate based on punctuation presentin the text sample. For example, the algorithm could segment the textsample based on tabs, carriage return, and/or some other punctuationpresent in the text such that each paragraph and/or sentence within thetext sample is assigned to a respective segment.

Additionally or alternatively, such an algorithm could incorporatenatural language processing techniques in order to partition a sample oftext. For example, the algorithm could operate to select partitionboundaries to minimize a cost function that is related to the semanticsimilarity between the words within each segment. This could includeminimizing a variance between the word vectors of the words within eachsegment.

In another example, a machine learning algorithm (e.g., an ANN) could betrained to receive samples of text and to generate segments therefrom.Such a machine learning algorithm could be trained to detectsemantically-related segments of text within larger samples of text bybeing provided with training data that comprises examples of shortsamples of text that are related to a single concept. For example, themachine learning algorithm could be provided with a set ofpreviously-received text queries (e.g., problem description fields fromincident reports) and could be trained to identify segments withinsamples of text that exhibit similar properties. In another example, themachine learning algorithm could be trained to recognize words thatprecede punctuation (e.g., !?.) that signal the end of the sentence,such that segments may be determined based only that punctuation whileavoiding segmentation based on non-sentence-ending punctuation (e.g.,the period in “Dr.”). In yet another example, the machine learningalgorithm could be trained to detect part of speech tags in order todetect the extent of sentences (e.g., for languages that do not havepunctuation).

VII. EXAMPLE OPERATIONS

FIG. 11 is a flow chart illustrating an example embodiment. The processillustrated by FIG. 11 may be carried out by a computing device, such ascomputing device 100, and/or a cluster of computing devices, such asserver cluster 200. However, the process can be carried out by othertypes of devices or device subsystems. For example, the process could becarried out by a portable computer, such as a laptop or a tablet device.

The embodiments of FIG. 11 may be simplified by the removal of any oneor more of the features shown therein. Further, these embodiments may becombined with features, aspects, and/or implementations of any of theprevious figures or otherwise described herein.

The example embodiment of FIG. 11 includes obtaining, by a serverdevice, a corpus of textual records (1100). Each of the textual recordsincludes a sample of text and an indication of one or more segments oftext within the sample of text. At least one of the textual recordsincludes an indication of at least two segments of text within thesample of text.

The example embodiment of FIG. 11 additionally includes obtaining, bythe server device and from a client device, a target sample of text(1102).

The example embodiment of FIG. 1 additionally includes determining, bythe server device, semantic similarity values between the target sampleof text and each of the textual records (1104). Determining a particularsemantic similarity value between the target sample of text and aparticular textual record of the corpus includes: (i) determiningindividual semantic similarity values between the target sample of textand each of the segments of text indicated by the particular textualrecord, and (ii) generating the particular semantic similarity valuebetween the target sample of text and the particular textual recordbased on the individual semantic similarity values.

The example embodiment of FIG. 11 additionally includes based on thesemantic similarity values, selecting, by the server device and from thecorpus, a textual record with a semantic similarity indicating that thetextual record is more similar to the target sample of text than anyother of the textual records (1106).

The example embodiment of FIG. 11 additionally includes providing, bythe server device and to the client device, a representation of thetextual record (1108).

VIII. CONCLUSION

The present disclosure is not to be limited in terms of the particularembodiments described in this application, which are intended asillustrations of various aspects. Many modifications and variations canbe made without departing from its scope, as will be apparent to thoseskilled in the art. Functionally equivalent methods and apparatuseswithin the scope of the disclosure, in addition to those describedherein, will be apparent to those skilled in the art from the foregoingdescriptions. Such modifications and variations are intended to fallwithin the scope of the appended claims.

The above detailed description describes various features and operationsof the disclosed systems, devices, and methods with reference to theaccompanying figures. The example embodiments described herein and inthe figures are not meant to be limiting. Other embodiments can beutilized, and other changes can be made, without departing from thescope of the subject matter presented herein. It will be readilyunderstood that the aspects of the present disclosure, as generallydescribed herein, and illustrated in the figures, can be arranged,substituted, combined, separated, and designed in a wide variety ofdifferent configurations.

With respect to any or all of the message flow diagrams, scenarios, andflow charts in the figures and as discussed herein, each step, block,and/or communication can represent a processing of information and/or atransmission of information in accordance with example embodiments.Alternative embodiments are included within the scope of these exampleembodiments. In these alternative embodiments, for example, operationsdescribed as steps, blocks, transmissions, communications, requests,responses, and/or messages can be executed out of order from that shownor discussed, including substantially concurrently or in reverse order,depending on the functionality involved. Further, more or fewer blocksand/or operations can be used with any of the message flow diagrams,scenarios, and flow charts discussed herein, and these message flowdiagrams, scenarios, and flow charts can be combined with one another,in part or in whole.

A step or block that represents a processing of information cancorrespond to circuitry that can be configured to perform the specificlogical functions of a herein-described method or technique.Alternatively or additionally, a step or block that represents aprocessing of information can correspond to a module, a segment, or aportion of program code (including related data). The program code caninclude one or more instructions executable by a processor forimplementing specific logical operations or actions in the method ortechnique. The program code and/or related data can be stored on anytype of computer readable medium such as a storage device including RAM,a disk drive, a solid state drive, or another storage medium.

The computer readable medium can also include non-transitory computerreadable media such as computer readable media that store data for shortperiods of time like register memory and processor cache. The computerreadable media can further include non-transitory computer readablemedia that store program code and/or data for longer periods of time.Thus, the computer readable media may include secondary or persistentlong term storage, like ROM, optical or magnetic disks, solid statedrives, compact-disc read only memory (CD-ROM), for example. Thecomputer readable media can also be any other volatile or non-volatilestorage systems. A computer readable medium can be considered a computerreadable storage medium, for example, or a tangible storage device.

Moreover, a step or block that represents one or more informationtransmissions can correspond to information transmissions betweensoftware and/or hardware modules in the same physical device. However,other information transmissions can be between software modules and/orhardware modules in different physical devices.

The particular arrangements shown in the figures should not be viewed aslimiting. It should be understood that other embodiments can includemore or less of each element shown in a given figure. Further, some ofthe illustrated elements can be combined or omitted. Yet further, anexample embodiment can include elements that are not illustrated in thefigures.

While various aspects and embodiments have been disclosed herein, otheraspects and embodiments will be apparent to those skilled in the art.The various aspects and embodiments disclosed herein are for purpose ofillustration and are not intended to be limiting, with the true scopebeing indicated by the following claims.

What is claimed is:
 1. A system comprising: a processor; and a memory,accessible by the processor, the memory storing instructions that, whenexecuted by the processor, cause the processor to perform operationscomprising: accessing a corpus comprising a plurality of textualrecords; generating, via a machine learning model, indications of one ormore respective segments of text within each of the textual records inthe corpus; obtaining, from a client device, a target sample of text;generating respective record semantic similarity values between thetarget sample of text and each of the textual records in the corpus,comprising, for each of the textual records in the corpus: determiningone or more respective segment semantic similarity values between thetarget sample of text and the one or more segments of text within thetextual record; and generating the respective record semantic similarityvalue between the target sample of text and the textual record based onthe one or more respective segment semantic similarity values; selectingfrom the corpus, based on the generated record semantic similarityvalues, a particular textual record having the highest respective recordsemantic similarity value for the target sample of text; and providing,to the client device, a representation of the particular textual record.2. The system of claim 1, wherein determining the one or more respectivesegment semantic similarity values between the target sample of text andthe one or more segments of text within the textual record comprises:receiving a vector representation of the target sample of text, whereinthe vector representation of the target sample of text includes wordvectors that describe, in a first semantically-encoded vector space, ameaning of respective words of the target sample of text, or a paragraphvector that describes, in a second semantically-encoded vector space, ameaning of multiple words of the target sample of text, or both;receiving one or more vector representations of the one or more segmentsof text within the textual record, wherein the one or more vectorrepresentations of the one or more segments of text within the textualrecord comprises word vectors that describe, in the firstsemantically-encoded vector space, a meaning of respective words of theone or more segments of text within the textual record, or a paragraphvector that describes, in the second semantically-encoded vector space,a meaning of multiple words within the one or more segments of textwithin the textual record; and determining a vector semantic similarityvalue between the vector representation of the target sample of text andthe vector representation of the one or more segments of text within thetextual record.
 3. The system of claim 1, wherein generating therespective record semantic similarity value between the target sample oftext and the textual record based on the one or more respective segmentsemantic similarity values comprises: comparing, to a thresholdsimilarity level, each of the one or more segment semantic similarityvalues between the target sample of text and each of the one or moresegments of text within the textual record; and determining a number ofthe one or more segment semantic similarity values that exceed thethreshold similarity level as the respective record semantic similarityvalue.
 4. The system of claim 1, wherein the indications of the one ormore respective segments of text within each of the textual recordscomprise non-overlapping segments of text.
 5. The system of claim 1,wherein the one or more respective segments of text within each of thetextual records comprise one or more discrete sentences.
 6. The systemof claim 1, wherein generating the respective record semantic similarityvalue between the target sample of text and the textual record based onthe one or more respective segment semantic similarity values comprises:weighting the one or more respective segment semantic similarity valuesbased on a ranking of the one or more respective segment semanticsimilarity values; and generating a sum of the weighted one or morerespective segment semantic similarity values between the target sampleof text and each of the one or more segments of text within the textualrecord.
 7. The system of claim 1, wherein each of the textual recordscomprises an indication of a time stamp within a predetermined timethreshold.
 8. A computer-implemented method comprising: accessing, by aserver device, a corpus comprising a plurality of textual records;generating indications of one or more respective segments of text withineach of the textual records in the corpus; receiving, by the serverdevice and from a client device, a target sample of text; generatingrespective record, by the server device, semantic similarity valuesbetween the target sample of text and each of the textual records in thecorpus, comprising, for each of the textual records in the corpus:determining one or more respective segment semantic similarity valuesbetween the target sample of text and the one or more segments of textwithin the textual record; and generating the respective record semanticsimilarity value between the target sample of text and the textualrecord based on the one or more respective segment semantic similarityvalues; selecting from the corpus, based on the generated recordsemantic similarity values, a particular textual record having thehighest respective semantic similarity value for the target sample oftext; and providing, by the server device and to the client device, arepresentation of the particular textual record.
 9. Thecomputer-implemented method of claim 8, wherein determining the one ormore respective segment semantic similarity values between the targetsample of text and the one or more segments of text within the textualrecord comprises: receiving a vector representation of the target sampleof text, wherein the vector representation of the target sample of textincludes word vectors that describe, in a first semantically-encodedvector space, a meaning of respective words of the target sample oftext, or a paragraph vector that describes, in a secondsemantically-encoded vector space, a meaning of multiple words of thetarget sample of text, or both; receiving one or more vectorrepresentations of the one or more segments of text within the textualrecord, wherein the one or more vector representations of the one ormore segments of text within the textual record comprises word vectorsthat describe, in the first semantically-encoded vector space, a meaningof respective words of the one or more segments of text within thetextual record, or a paragraph vector that describes, in the secondsemantically-encoded vector space, a meaning of multiple words withinthe one or more segments of text within the textual record; anddetermining a vector semantic similarity value between the vectorrepresentation of the target sample of text and the vectorrepresentation of the one or more segments of text within the textualrecord.
 10. The computer-implemented method of claim 8, whereingenerating the respective record semantic similarity value between thetarget sample of text and the textual record based on the one or morerespective segment semantic similarity values comprises: comparing, to athreshold similarity level, each of the one or more segment semanticsimilarity values between the target sample of text and each of the oneor more segments of text within the textual record; and determining anumber of the one or more segment semantic similarity values that exceedthe threshold similarity level as the respective record semanticsimilarity value.
 11. The computer implemented method of claim 8,wherein generating indications of the one or more respective segments oftext within each of the textual records in the corpus is based on one ormore document properties, and wherein the method comprises partitioning,based on the indications, each of the textual records into the one ormore segments of text.
 12. The computer-implemented method of claim 8,wherein the indications of the one or more respective segments of textwithin each of the textual records comprise non-overlapping segments oftext.
 13. The computer-implemented method of claim 8, wherein the one ormore respective segments of text within each of the textual recordsrepresent one or more discrete sentences.
 14. The computer-implementedmethod of claim 8, comprising: based on the generated record semanticsimilarity values, selecting, from the corpus, two or more textualrecords; and providing, to the client device, a representation of thetwo or more textual records.
 15. An article of manufacture including anon-transitory computer-readable medium, having stored thereon programinstructions that, upon execution by a computing system, cause thecomputing system to perform operations comprising: accessing, by aserver device, a corpus comprising a plurality of textual records;generating indications of one or more respective segments of text withineach of the textual records in the corpus; receiving, by the serverdevice and from a client device, a target sample of text; generatingrespective record, by the server device, semantic similarity valuesbetween the target sample of text and each of the textual records in thecorpus, comprising, for each of the textual records in the corpus:determining one or more respective segment semantic similarity valuesbetween the target sample of text and the one or more segments of textwithin the textual record; and generating the respective record semanticsimilarity value between the target sample of text and the textualrecord based on the one or more respective segment semantic similarityvalues; selecting from the corpus, based on the generated recordsemantic similarity values, a particular textual record having thehighest respective semantic similarity value for the target sample oftext; and providing, by the server device and to the client device, arepresentation of the particular textual record.
 16. The article ofmanufacture of claim 15, wherein determining the one or more respectivesegment semantic similarity values between the target sample of text andthe one or more segments of text within the textual record comprises:receiving a vector representation of the target sample of text, whereinthe vector representation of the target sample of text includes wordvectors that describe, in a first semantically-encoded vector space, ameaning of respective words of the target sample of text, or a paragraphvector that describes, in a second semantically-encoded vector space, ameaning of multiple words of the target sample of text, or both;receiving one or more vector representations of the one or more segmentsof text within the textual record, wherein the one or more vectorrepresentations of the one or more segments of text within the textualrecord comprises word vectors that describe, in the firstsemantically-encoded vector space, a meaning of respective words of theone or more segments of text within the textual record, or a paragraphvector that describes, in the second semantically-encoded vector space,a meaning of multiple words within the one or more segments of textwithin the textual record; and determining a vector semantic similarityvalue between the vector representation of the target sample of text andthe vector representation of the one or more segments of text within thetextual record.
 17. The article of manufacture of claim 15, whereingenerating the respective record semantic similarity value between thetarget sample of text and the textual record based on the one or morerespective segment semantic similarity values comprises: comparing, to athreshold similarity level, each of the one or more segment semanticsimilarity values between the target sample of text and each of the oneor more segments of text within the textual record; and determining anumber of the one or more segment semantic similarity values that exceedthe threshold similarity level as the respective record semanticsimilarity value.
 18. The article of manufacture of claim 15, whereineach of the textual records comprises an indication of a time stampwithin a predetermined time threshold.
 19. The article of manufacture ofclaim 15, wherein the indications of the one or more respective segmentsof text within each of the textual records comprise non-overlappingsegments of text.
 20. The article of manufacture of claim 15, whereinthe operations further comprise: based on the generated record semanticsimilarity values, selecting, from the corpus, two or more textualrecords; and providing, to the client device, a representation of thetwo or more textual records.